CLOUD-BASED SELF-SERVICE BUSINESS INTELLIGENCE PLATFORMS: A COMPREHENSIVE REVIEW

Cloud-based self-service business intelligence (SSBI) platforms have become integral to contemporary analytics strategies as organizations pursue faster, user-driven decision-making in distributed data environments.Unlike traditional BI systems that rely on centralized IT control, cloud-based SSBI enables business users to independently explore and analyse data, improving analytical agility and scalability.However, this transition also introduces challenges related to data governance, consistency, cost control, and organizational accountability.This review synthesizes academic and industry literature published between 2019 and 2025 to examine how cloud-based SSBI platforms address the governance-agility trade-off.Drawing on peer-reviewed studies, market analyses, and cloud adoption reports, the paper evaluates governance mechanisms such as federated access models, metadata-driven semantic layers, certified datasets, and policy-based data stewardship.Rather than proposing a new architectural model, the study consolidates established governance practices into a structured analytical perspective that highlights recurring design patterns across widely adopted SSBI platforms.The findings indicate that sustainable SSBI adoption depends on governance models that balance user autonomy with centralized oversight.Platforms that embed governance without restricting exploratory analytics demonstrate higher trust and long-term analytical scalability.